How We Saved a Client $100,000 in PPC Spend

The digital pay-per-click advertising world is complex and full of nuances. By paying attention to the little details, you can save yourself and your client a huge headache (and potentially a lot of money).

One of our clients runs multiple comparison shopping engine campaigns on several of the top platforms available including Google Shopping, Bing Shopping, Connexity and more.

Our client spends upwards of $50,000 a month per network running these campaigns due to their ability to generate hundreds of thousands of dollars in revenue.

Tracking campaign performance is essential to ensuring our client is making money and so we implemented and ensured the proper tracking and reporting of Google Analytics Ecommerce Tracking as well as each platform’s independent ROI tracker.

With two data independent reporting sources, we were able to compare reports and see if they were in agreement with each other. We regularly checked both Analytics and independent ROI codes and they were always close enough to provide assurance that they were tracking accurately. We had faith in these tracking codes and no reason to believe otherwise. Little did we know what we were about to find out.

Our client operates in a B2B niche and therefore offers payment methods that wouldn’t be standard in B2C e-commerce. These payment methods include Pay By Check and Purchase Order that can be chosen in the checkout in order to complete an order. Our client had always offered these payment methods since they’re preferred by the B2B clientele our client does business with.

By and large, most people (>90%) that used those two payment methods to check out would actually mail a check or post payment via some other method to pay for their order. However, a couple of months ago the client noticed something strange. First, the client told us that a large number of orders placed using the purchase order or check by mail payment method weren’t actually being paid. Meaning, no payment was actually received for those orders. Second, then we began to see a large spike in clicks and traffics from one of our PLA platforms even though it should have been trending downwards due to seasonal trends that generally affect our clients industry niche.

Even though payments were never received for these orders, Google Analytics and the platforms independent ROI tracking code both showed positive revenue from these orders because they would only grab and submit the order total data from the order confirmation page with no way of knowing if the orders were going to be paid.

So, how did we track down the issue?

First, we had to identify if there was some sort of commonality amongst all these fake orders. We did an export of all the orders and filtered them by payment method used, specifically looking at check by mail and purchase order payment method orders.

Here we noticed a few things. For instance, the purchase order numbers for many orders seemed to follow a strange, repeating format. Next, we noticed the shipping addresses for these orders were largely residential addresses (our client being B2B primarily shipped to commercial addresses). We also saw that these orders were being placed every day around the same time with increasing frequency. All three of these things combined raised a major red flag.

We reached out to the individuals who placed these orders only to find out when we called them by phone that they had never placed an order on our site before. Someone was using stolen personal information to place these orders on our store. This stunned us once we realized the breadth of the situation. Immediately, we asked ourselves a few questions.

Was a competitor trying to harm us by clouding our pay-per-click cost and revenue data?

Was there some sort of affiliate scheme whereby a comparison shopping engine network partner would get increased returns if their network provided increased sales as opposed to other network partners?

The next step was to build a concrete data set of the fake orders and attempt to see if they were originating from a single source. Here’s what we did:

We exported of all transactions dating back a year from Google Analytics. When we exported this data from Google Analytics, we made sure to export the transaction IDs, transaction amounts and the source/medium of the transaction. Then we did an export of orders from our e-commerce store dating back a year.

We opened up both exports in Excel and used the VLOOKUP formula to match them up via the unique transaction IDs and it quickly became clear what was going on.

98% of the fake orders were originating from one of the comparison shopping engine (PLA) platforms that we were using. This was evident when we filtered for orders that were paid using the check by mail or purchase order payment method and then matched up the source/medium information for the same orders using Analytics data.

Once we found the smoking gun, were quickly moved to analyze the size and scope of the fraudulent order amounts. We realized that we spent roughly $139,0000 and only made roughly $29,000 in revenue from this particular platform. Analytics and the platform’s ROI tracker had reported revenue of over $450,000. Except, it wasn’t real revenue or money in the bank for our client.

While the reporting looked great at first glance with a solid ROI, during the course of our investigation we realized we had actually lost tens of thousands of dollars on a campaign that we thought was performing exceedingly well.

It was at this point that we reached out to our account manager at the specific comparison shopping engine network and told them about our findings. We presented them with our dataset and laid a compelling case for click-fraud before them.

After a couple of days, they were able to identify which of their partners was sending the bad traffic to our store and blocked them.

They also attempted to shift blame onto us for allowing the purchase order and check by mail payment methods which are standard in our industry.

They further complicated the matter by blaming an incorrectly placed ROI tag on our checkout pages (it was correctly placed).

Once we successfully rebutted all their attempts at deflecting the main, it became pretty clear that we had a very solid argument and the data to back it up.

We met internally and with our client to discuss the best way forward. We knew we had a good click fraud case on our hands and could involve a lawyer. However, we decided to attempt to handle outside of a legal framework.

We asked our comparison shopping engine platform partner for a cash refund of the spend that we had spent with them over the past 6 months.

After some maneuvering and posturing we were able to secure a refund of over $100,000. Spend which was spent thinking we were making a positive return, when in fact we had been losing thousands.

Our client was happy with the end results and we were happy we were able to pinpoint the issue and facilitate a positive resolution.

We had never experienced this type of click fraud before, but we definitely learned a few valuable lessons. Primarily, not to trust automated reporting metrics but to take them with a grain of salt and verify them manually. Especially if certain numbers start to trend out of the ordinary against expectations. We also learned a few other lessons:

Here’s a few ways avoid experiencing an issue with fraudulent orders if you offer non-standard payment methods such as pay by check, purchase order, or pay in-store on your e-commerce store checkout. Make sure to choose the method that works best for your business.

Implement a captcha that must be entered by users whenever a specific payment method is chosen. This is effective at stopping robots or automated systems that may be placing the orders. This will not stop a human.

Require users to create an account on your store that must be pre-approved before allowing them to place orders using check by mail or purchase order payment methods. This method allows you to vet users before they can place an order on your store and allows you to quickly notice red flags such as residential shipping addresses for a B2B business.

Customize your Google Analytics e-commerce tracking code to add a custom dimension when orders are submitted with a specific payment method. This can help you segment traffic and reporting later on should you suspect an issue with a particular payment method.

Each of these methods has a few pitfalls that should be considered before implementation, but whatever method you choose is crucial to ensuring your campaigns are performing at the level that they appear to be performing at.

With large campaigns where tens or hundreds of thousands of dollars are spent every month, ensuring a positive ROI is critical to maintaining a business’s financial situation in the green as well as clients happy.

We hope you can learn from our experience and know better than to take reporting metrics for granted. Always double-check and manually verify campaign performance data from time to time to ensure your campaigns are actually profitable and don’t just seem like they are.